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Human-robot interaction (HRI) is an interdisciplinary field that utilises both quantitative and qualitative methods. While ROSBags, a file format within the Robot Operating System (ROS), offer an efficient means of collecting temporally synched multimodal data in empirical studies with real robots, there is a lack of tools specifically designed to integrate qualitative coding and analysis functions with ROSBags. To address this gap, we developed ROSAnnotator, a web-based application that incorporates a multimodal Large Language Model (LLM) to support both manual and automated annotation of ROSBag data. ROSAnnotator currently facilitates video, audio, and transcription annotations and provides an open interface for custom ROS messages and tools. By using ROSAnnotator, researchers can streamline the qualitative analysis process, create a more cohesive analysis pipeline, and quickly access statistical summaries of annotations, thereby enhancing the overall efficiency of HRI data analysis. https://github.com/CHRI-Lab/ROSAnnotator, Comment: Accepted to HRI 2025

Office Assistant Robots (OARs) offer a promising solution to proactively provide in-situ support to enhance employee well-being and productivity in office spaces. We introduce OfficeMate, a social OAR designed to assist with practical tasks, foster social interaction, and promote health and well-being. Through a pilot evaluation with seven participants in an office environment, we found that users see potential in OARs for reducing stress and promoting healthy habits and value the robot's ability to provide companionship and physical activity reminders in the office space. However, concerns regarding privacy, communication, and the robot's interaction timing were also raised. The feedback highlights the need to carefully consider the robot's appearance and behaviour to ensure it enhances user experience and aligns with office social norms. We believe these insights will better inform the development of adaptive, intelligent OAR systems for future office space integration., Comment: 5 pages, 1 figure, accepted to HRI 2025

Team projects in Computer Science (CS) help students build collaboration skills, apply theory, and prepare for real-world software development. Online classes present unique opportunities to transform the accessibility of CS education at scale. Still, the geographical distribution of students and staff adds complexity to forming effective teams, providing consistent feedback, and facilitating peer interactions. We discuss our approach of managing, evaluating, and providing constructive feedback to over 200 project teams, comprising 1000+ graduate students distributed globally, two professors, and 25+ teaching assistants. We deployed and iteratively refined this approach over 10 years while offering the Data and Visual Analytics course (CSE 6242) at Georgia Institute of Technology. Our approach and insights can help others striving to make CS education accessible, especially in online and large-scale settings., Comment: Poster at Technical Symposium on Computer Science Education (SIGCSE TS 2025), 2 pages

The increase in population and economic growth, coupled with accelerated urbanization and suburbanization, has exacerbated traffic congestion and environmental challenges in urban areas. To address these issues, a comprehensive traffic management program has been introduced, aimed at enhancing the regulation and control of traffic flow, thereby ensuring faster and safer travel. By leveraging data collected from various regions, tailored traffic management strategies can be implemented to meet the specific needs of different city sectors. This approach involves continuous monitoring of traffic conditions and the application of targeted interventions to mitigate congestion and improve overall traffic efficiency. A case study in Tehran exemplifies the application of this program. A designated section of the city's traffic network is being utilized to test the program's efficacy. The objective is to assess its practical effectiveness under real-world conditions and refine the program based on empirical findings. This initiative aims to provide a robust solution to Tehran's traffic challenges, contributing to improved traffic management and enhanced safety., Comment: 13 pages

We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rapid growth of scientific publications necessitates scalable and efficient methods for classification, further complicated by the evolving nature of taxonomies--where new categories are introduced, existing ones are merged, and outdated ones are deprecated. Traditional machine learning approaches, which require costly retraining with each taxonomy update, become impractical due to the high overhead of labelled data collection and model adaptation. Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification. However, applying them to large and dynamic taxonomies presents unique challenges as the vast number of labels can exceed LLMs' input limits. In this paper, we present novel methods that combine the strengths of LLMs with dense retrieval techniques to overcome these challenges. Our approach avoids retraining by leveraging zero-shot HMC for real-time label assignment. We evaluate the effectiveness of our methods on SSRN, a large repository of preprints spanning multiple disciplines, and demonstrate significant improvements in both classification accuracy and cost-efficiency. By developing a tailored evaluation framework for dynamic taxonomies and publicly releasing our code, this research provides critical insights into applying LLMs for document classification, where the number of classes corresponds to the number of nodes in a large taxonomy, at an industrial scale., Comment: This paper has been accepted at COLING 2025 (Industry Track)

We studied bent radio sources within X-ray galaxy groups in the COSMOS and XMM-LSS fields, using radio data from the MeerKAT International GHz Tiered Extragalactic Explorations data release 1 (MIGHTEE-DR1) at 1.2-1.3 GHz (angular resolutions of 8.9" and 5"; ~ 3.5 and 5.5 uJy/beam). Bent radio active galactic nuclei (AGN) were identified via visual inspection. Our analysis included 19 bent radio AGN in the COSMOS field and 17 in the XMM-LSS field which lie within X-ray galaxy groups (2x10^13 >= M200c/Msun = 3x10^14). We investigated the relationship between their bending angle (BA) - the angle formed by the jets or lobes of two-sided radio sources associated with AGN - and properties of their host galaxies and large-scale environment probed by the X-ray galaxy groups. Our key findings are: a) In the XMM-LSS field, we observed a strong correlation between the linear projected size of the bent AGN, the group halo mass, and the projected distance from the group centre. This trend, consistent with previous studies, was not detected in the COSMOS sample. b) The BA is a function of environmental density, with the type of medium playing a significant role. Additionally, at z <= 0.5 we found a higher number of bent sources (BA <= 160deg) compared to higher redshifts (z ~ 1), by a factor of >1.5. This trend aligns with magnetohydrodynamic simulations, which suggest that denser environments and longer interaction times at lower redshifts contribute to this effect. Comparison with the literature suggests that jet bending in galaxy groups within the redshift range 0.1 < z < 1.2 is primarily driven by ram pressure exerted on the jets, which occurs during quiescent phases of AGN activity. This study underscores the role of environmental interactions in shaping the morphology of radio AGN within galaxy groups, providing insights into the interplay between large-scale structure and AGN physics., Comment: 25 pages, 12 Figures, 9 Tables, 1 Appendix; submitted to A&A

Understanding mass, size, and surface mass density of giant molecular clouds (GMCs) in galaxies is key to insights into star formation processes. We analyze these in M33 using Herschel dust and archival IRAM 30m telescope data, compared to Milky Way CO data. A Dendrogram algorithm on a 2D dust map and a Xco factor map are used for M33 instead of a constant value. Dust and CO-derived values are similar, with mean radii of $\sim\,$$58\,$pc for the dust and $\sim\,$$68\,$pc for CO. Largest GMAs are about $150\,$pc in radius, similar to the Milky Way, suggesting a size-limiting process. M33 contains less massive, lower-density GMCs compared to the Milky Way. The highest mass GMCs observed in the Milky Way are mostly absent in M33. M33's mean surface mass density is much lower, due to the Milky Way's higher column densities despite similar GMC areas. No systematic gradients in M33's physical properties were found with galactocentric radius, but higher densities and masses near the center suggest increased star formation. In both galaxies, 30% of molecular mass is central. The GMC mass power-law spectrum index is $\alpha=2.3\pm0.1$ and $\alpha=1.9\pm0.1$ for dust and CO in M33, respectively. We conclude that M33 and Milky Way GMCs are mostly similar, though M33 lacks high-mass GMCs, with no clear explanation. GMC properties weakly depend on galactic environment, with stellar feedback as a factor needing further study.

The millimeter continuum emission from galaxies provides important information about cold dust, its distribution, heating, and role in their InterStellar Medium (ISM). This emission also carries an unknown portion of the free-free and synchrotron radiation. The IRAM 30m Guaranteed Time Large Project, Interpreting Millimeter Emission of Galaxies with IRAM and NIKA2 (IMEGIN) provides a unique opportunity to study the origin of the millimeter emission on angular resolutions of <18" in a sample of nearby galaxies. As a pilot study, we present millimeter observations of two IMEGIN galaxies, NGC 2146 (starburst) and NGC 2976 (peculiar dwarf) at 1.15 mm and 2 mm. Combined with the data taken with Spitzer, Herschel, Plank, WSRT, and the 100m Effelsberg telescopes, we model the infrared-to-radio Spectral Energy Distribution (SED) of these galaxies, both globally and at resolved scales, using a Bayesian approach to 1) dissect different components of the millimeter emission, 2) investigate the physical properties of dust, and 3) explore correlations between millimeter emission, gas, and Star Formation Rate (SFR). We find that cold dust is responsible for most of the 1.15 mm emission in both galaxies and at 2 mm in NGC 2976. The free-free emission emits more importantly in NGC 2146 at 2 mm. The cold dust emissivity index is flatter in the dwarf galaxy ($\beta = 1.3\pm 0.1$) compared to the starburst galaxy ($\beta = 1.7\pm 0.1$). Mapping the dust-to-gas ratio, we find that it changes between 0.004 and 0.01 with a mean of $0.006\pm0.001$ in the dwarf galaxy. In addition, no global balance holds between the formation and dissociation of H$_2$ in this galaxy. We find tight correlations between the millimeter emission and both the SFR and molecular gas mass in both galaxies., Comment: Accepted for publication in Astronomy & Astrophysics

Artifacts often degrade the visual quality of virtual try-on (VTON) and pose transfer applications, impacting user experience. This study introduces a novel conditional inpainting technique designed to detect and remove such distortions, improving image aesthetics. Our work is the first to present an end-to-end framework addressing this specific issue, and we developed a specialized dataset of artifacts in VTON and pose transfer tasks, complete with masks highlighting the affected areas. Experimental results show that our method not only effectively removes artifacts but also significantly enhances the visual quality of the final images, setting a new benchmark in computer vision and image processing.

Resolved studies of the correlation between the radio and far-infrared (FIR) emission from galaxies at different frequencies can unveil the interplay between star formation and relativistic interstellar medium (ISM). Thanks to the LOFAR LoTSS observations combined with the VLA, Herschel, and WISE data, we study the role of the cosmic rays and magnetic fields in the radio-FIR correlation on scales of ~> 200 pc in the nearby galaxy IC342. The thermal emission traced by the 22 micron emission, constitutes about 6%, 13%, and 30% of the observed radio emission at 0.14, 1.4, 4.8 GHz, respectively, in star forming regions and less in other parts. The nonthermal spectral index becomes flatter at frequencies lower than 1.4 GHz (a=-0.51 +- 0.09, S(nu)~ nu^(a)) than between 1.4 and 4.8 GHz (a = -1.06+- 0.19) on average and this flattening occurs not only in star-forming regions but also in diffuse ISM. The radio-FIR correlation holds at all radio frequencies; however, it is tighter at higher radio frequencies. A multi-scale analysis shows that this correlation cannot be maintained on small scales due to diffusion of cosmic ray electrons (CREs). The correlation breaks on a larger scale (320 pc) at 0.14 GHz than at 1.4 GHz (200 pc) indicating that those CREs traced at lower frequencies have diffused a longer path in the ISM. We find that the energy index of CREs becomes flatter in star forming regions in agreement with previous studies. Cooling of CREs due to the magnetic field is evident globally only after compensating for the effect of star formation activity which both accelerate CREs and amplify magnetic fields. Compared with other nearby galaxies, it is shown that the smallest scale of the radio-FIR correlation is proportional to the CREs propagation length on which the ordered magnetic field has an important effect., Comment: 22 pages, 15 figures, accepted for publication in Astronomy & Astrophysics Journal

Evolving networks are complex data structures that emerge in a wide range of systems in science and engineering. Learning expressive representations for such networks that encode their structural connectivity and temporal evolution is essential for downstream data analytics and machine learning applications. In this study, we introduce a self-supervised method for learning representations of temporal networks and employ these representations in the dynamic link prediction task. While temporal networks are typically characterized as a sequence of interactions over the continuous time domain, our study focuses on their discrete-time versions. This enables us to balance the trade-off between computational complexity and precise modeling of the interactions. We propose a recurrent message-passing neural network architecture for modeling the information flow over time-respecting paths of temporal networks. The key feature of our method is the contrastive training objective of the model, which is a combination of three loss functions: link prediction, graph reconstruction, and contrastive predictive coding losses. The contrastive predictive coding objective is implemented using infoNCE losses at both local and global scales of the input graphs. We empirically show that the additional self-supervised losses enhance the training and improve the model's performance in the dynamic link prediction task. The proposed method is tested on Enron, COLAB, and Facebook datasets and exhibits superior results compared to existing models., Comment: 16 pages, 6 figures

In quantum dot junctions capacitively coupled to a resonator, electron tunneling through the quantum dot can be used to transfer heat between different parts of the system. This includes cooling or heating the electrons in electrodes and absorbing or emitting photons in the resonator mode. Such systems can be driven into a nonequilibrium state by applying either a voltage bias or a temperature gradient across the electrodes coupled to the quantum dot, or by employing an external coherent pump to excite the resonator. In this study, we present a semiclassical theory to describe the steady state of these structures. We employ a combination of the Floquet-nonequilibrium Green's functions method and semiclassical laser theory to analyze a normal metal-quantum dot-superconductor junction coupled to a resonator. Our investigation focuses on key parameters such as the average photon number and phase shift in the resonator, the charge current in the quantum dot, and the heat fluxes among different components of the system. We explore how photon-assisted Andreev reflection and quasiparticle tunneling in the quantum dot can refrigerate the resonator mode and the normal metal electrode. We also examine the influence of finite voltage and thermal biases on these processes.

In order to develop a more comprehensive picture of star formation, it is essential to understand the physical relationship between dense cores and the filaments embedding them. There is evidence that magnetic fields play a crucial role in this context. We aim to understand how magnetic fields influence the properties and kinematics of an isolated filament located east of the Barnard 59 clump, belonging to the Pipe Nebula. We use near infrared polarization observations to determine the magnetic field configuration, and we apply the Davis Chandrasekhar Fermi method to infer the magnetic field strength in the plane of the sky. Furthermore, we use complementary data from the James Clerk Maxwell Submillimetre Telescope (JCMT) of C18O and 13CO J=3-2 transition to determine the filament's kinematics. Finally, we model the radial density profile of the filament with polytropic cylindrical models. Our results indicate that the filament is stable to radial collapse and is radially supported by agents other than thermal pressure. In addition, based on previous observations of emission lines on this source, we suggest that gas is flowing toward the hub, while C18O (3-2) non-thermal motions indicate that the cloud is in a quiescent state., Comment: 12 pages, accepted at A&A journal

We present a silicon-nitride-based optical phased array with built-in focusing and steering capability, that operates at 522 nm and is aimed at complementing a micro-electrode array for joint electrical and optical probing of retinal tissue. It achieves subcellular resolution with a beam diameter of 1.4 um at a focal point located above the chip. Targeted cellular excitation can be achieved by steering the beam through a combination of wavelength tuning and simplified thermo-optical phase shifters with a single electrical input for each of transverse beam steering and selection of the focal plane.

The McVittie-Plummer spacetime is a spherically symmetric inhomogeneous cosmological model that represents a spherical star system embedded in a standard FLRW cosmological model. We study the main physical properties of this gravitational field. Regarding the interplay between the physics of the local system and the expanding background, we employ the Misner-Sharp mass-energy function to show that there is a relatively weak time-dependent general relativistic coupling between the astrophysical system and the background FLRW cosmological model. The coupling term is proportional to the inverse of the scale factor and decreases as the universe expands., Comment: 21 pages; v2: expanded version accepted for publication in ApJ; v3: typos corrected

This study is aimed to contribute to a more comprehensive understanding of the molecular hydrogen distribution in the galaxy M33 by introducing novel methods for generating high angular resolution (18.2$''$, equivalent to 75 pc) column density maps of molecular hydrogen ($N_{\rm H_2}$). M33 is a local group galaxy that has been observed with Herschel in the far-infrared wavelength range from 70 to 500 $\mu$m. Previous studies have presented total hydrogen column density maps ($N_{\rm H}$), using these FIR data (partly combined with mid-IR maps), employing various methods. We first performed a spectral energy distribution fit to the 160, 250, 350, and 500 $\mu$m continuum data obtain $N_{\rm H}$, using a technique similar to one previously reported in the literature. We also use a second method which involves translating only the 250 $\mu$m map into a $N_{\rm H}$ map at the same angular resolution. An $N_{\rm H_2}$ map via each method is then obtained by subtracting the HI component. Distinguishing our study from previous ones, we adopt a more versatile approach by considering a variable emissivity index, $\beta$ and dust absorption coefficient, $\kappa_0$. This choice enables us to construct a $\kappa_0$ map, thereby enhancing the depth and accuracy of our investigation of the hydrogen column density. We address the inherent biases and challenges within both methods (which give similar results) and compare them with existing maps available in the literature. Moreover, we calculate a map of the carbon monoxide CO-to-H$_2$ conversion factor ($X_\mathrm{CO}$ factor), which shows a strong dispersion around an average value of $1.8\times10^{20}\,\mathrm{cm^{-2}/(K\,km\,s^{-1})}$ throughout the disk. We obtain column density probability distribution functions (N-PDFs) from the $N_{\rm H}$, $N_{\rm H_2}$, and $N_{HI}$ maps and discuss their shape.

Synthetic data generation is increasingly recognized as a crucial solution to address data related challenges such as scarcity, bias, and privacy concerns. As synthetic data proliferates, the need for a robust evaluation framework to select a synthetic data generator becomes more pressing given the variety of options available. In this research study, we investigate two primary questions: 1) How can we select the most suitable synthetic data generator from a set of options for a specific purpose? 2) How can we make the selection process more transparent, accountable, and auditable? To address these questions, we introduce a novel approach in which the proposed ranking algorithm is implemented as a smart contract within a permissioned blockchain framework called Sawtooth. Through comprehensive experiments and comparisons with state-of-the-art baseline ranking solutions, our framework demonstrates its effectiveness in providing nuanced rankings that consider both desirable and undesirable properties. Furthermore, our framework serves as a valuable tool for selecting the optimal synthetic data generators for specific needs while ensuring compliance with data protection principles.

Dynamic nuclear polarization (DNP) has revolutionized the field of NMR spectroscopy, expanding its reach and capabilities to investigate diverse materials, biomolecules, and complex dynamic processes. Bringing high-efficiency DNP to the nanometer scale would open new avenues for studying nanoscale nuclear spin ensembles, such as single biomolecules, virus particles, and condensed matter systems. Combining pulsed DNP with nanoscale force-detected magnetic resonance measurements, we demonstrated a 100-fold enhancement in the Boltzmann polarization of proton spins in nanoscale sugar droplets at 6 K and 0.33 T. Crucially, this enhancement corresponds to a factor of 200 reduction in the averaging time compared to measurements that rely on the detection of statistical fluctuations in nanoscale nuclear spin ensembles. These results significantly advance the capabilities of force-detected magnetic resonance detection as a practical tool for nanoscale imaging., Comment: Main text: 26 pages, 5 figures, 3 tables; supplemental material: 36 pages, 15 figures, 4 tables

Optical frequency division (OFD) has revolutionized microwave and mmWave generation and set spectral purity records owing to its unique capability to transfer high fractional stability from optical to electronic frequencies. Recently, rapid developments in integrated optical reference cavities and microresonator-based optical frequency combs (microcombs) have created a path to transform OFD technology to chip scale. Here, we demonstrate an ultra-low phase noise mmWave oscillator by leveraging integrated photonic components and Kerr optical frequency division. The oscillator derives its stability from an integrated CMOS-compatible SiN coil cavity, and the optical frequency division is achieved spontaneously through Kerr interaction between the injected reference lasers and soliton microcombs in the integrated SiN microresonator. Besides achieving record-low phase noise for integrated mmWave oscillators, our demonstration greatly simplifies the implementation of integrated OFD oscillators and could be useful in applications of Radar, spectroscopy, and astronomy., Comment: 8 pages, 5 figures

Recent experiments show that the ground state of some of the layered materials with localized moments is in close proximity to the Kitaev spin liquid, calling for a proper model to describe the measurements. The Kitaev-Hubbard model (KHM) is the minimal model that captures the essential ingredients of these systems, namely it yields the Kitaev-Heisenberg spin model at the strong couling limit and contains the charge fluctuations present in these materials as well. Despite its relevance, the phase diagram of the KHM has not been rigorously revealed yet. In this work, we study the full phase diagram of the KHM using the $\mathbb{Z}_2$ slave-spin mean-field theory as well as the auxiliary field quantum Monte Carlo (QMC) on rather large systems and at low temperatures. The Mott transition is signaled by a vanishing quasiparticle weight evaluated using the slave-spin construction. Moreover, we show using both QMC and slave spin approaches that there are multiple magnetic phase transitions within the Mott phase including magnetically ordered phases and most notably a spin liquid phase consistent with recent studies of the KHM., Comment: 6 pages, 6 figures

The field of nanoscale magnetic resonance imaging (NanoMRI) was started 30 years ago. It was motivated by the desire to image single molecules and molecular assemblies, such as proteins and virus particles, with near-atomic spatial resolution and on a length scale of 100 nm. Over the years, the NanoMRI field has also expanded to include the goal of useful high-resolution nuclear magnetic resonance (NMR) spectroscopy of molecules under ambient conditions, including samples up to the micron-scale. The realization of these goals requires the development of spin detection techniques that are many orders of magnitude more sensitive than conventional NMR and MRI, capable of detecting and controlling nanoscale ensembles of spins. Over the years, a number of different technical approaches to NanoMRI have emerged, each possessing a distinct set of capabilities for basic and applied areas of science. The goal of this roadmap article is to report the current state of the art in NanoMRI technologies, outline the areas where they are poised to have impact, identify the challenges that lie ahead, and propose methods to meet these challenges. This roadmap also shows how developments in NanoMRI techniques can lead to breakthroughs in emerging quantum science and technology applications.